In the fields of medicine, computer aided design, and computer aided modeling, it is often necessary to generate computer models of real objects. For example, in the field of medicine, images of anatomical structures are obtained using magnetic resonance imaging (MRI) and computer tomography (CT). Both MRI and CT images are used for diagnostic and treatment purposes. For example, in the field of radiation oncology, CT images are used by physicians to determine the appropriate radiation dosage amount and dosage area for patients undergoing radiation therapy. In radiation therapy, it is desirable that the dosage amount be tailored to the size of the tumor being treated and focused only on the area of the tumor being treated since radiation can harm healthy cells.
Conventional MRI- and CT-based methods for determining radiation therapy dosage areas and amounts involve examining MRI and CT images, manually drawing a map of the dosage area on the images, and making the corresponding marks on the patient on which to focus the radiation beam. While these manual methods are somewhat effective in determining the appropriate dosage amount, it is desirable to improve the accuracy of these methods and decrease the time required to plan a dosage amount. Thus, radiation therapy is one field that would benefit from improved object and image modeling techniques.
On example of an object and image modeling system is disclosed in U.S. Pat. No. 5,926,568 to Chaney et al. (hereinafter, “the '568 patent”), the disclosure of which is incorporated herein by reference in its entirety. According to the '568 patent, automatic image recognition of standard shapes is performed using deformable segments, the changes of which are measurable against a deformed model corresponding to an object in a subsequent image. Statistical correlation techniques optimize the match to further refine the shape of the subsequent image. While the methods and systems disclosed in the '568 patent decrease the time and improve the accuracy of image object matching, the image and object models disclosed therein are two-dimensional. Thus, improved tools for modeling three-dimensional structures and for deforming three-dimensional models into image data are needed.
Conventional computer-based modeling methods for three-dimensional structures involve constructing meshes that represent the surface of the object being modeled. Each point on the surface of a mesh is represented by a point in a model-independent coordinate system, such as a Cartesian coordinate system, a polar coordinate system, or a spherical coordinate system. Representing each point on the surface of a mesh with a model-independent coordinate system greatly increases the difficulty in comparing models to target images, determining correspondence between models, and deforming the surfaces of models. For example, it may be desirable to perform natural actions, such as increasing or decreasing the girth of a model, bending the model, elongating the model, or twisting the model. The mathematical computations required for performing these actions using a model-independent coordinate system greatly increase processing time required for these operations because each point must be independently moved. Accordingly, there exists a long felt need for improved methods and systems for modeling objects and object image data and matching models to target image data.